Implementing Component Degradations into a Modelica Model of an iPWR System to Develop Health Monitoring Techniques

Figures & data

Fig. 1. NPM in the Dymola environment.

Fig. 2. Diagram of a simplified globe valve and hydraulic actuator.

Fig. 3. Degradation models within the BOP model in Dymola.

Fig. 4. Power profile and leaking mass flow rate.

Fig. 5. Multiplication factor for actuator degradation.

Fig. 6. Flow diagram of the fault detection and classification algorithm.

Fig. 7. Membership example.

Fig. 8. Validation of fault detection and fuzzy classification methods using healthy LF data.

TABLE I Expected Variable Membership for Each Reactor State

Variable	Healthy	Leaking	Actuator
HPT^a output	Normal	Low	Normal
LPT^b output	Normal	Low	Normal
TCV response	Normal	Low	High
TCV pressure	Normal	Low	Normal
Condenser mass	Normal	Low	Normal
TCV 1 flow rate	Normal	Low	High
TCV 2 flow rate	Normal	Low	High
TCV 3 flow rate	Normal	Low	Low

^aHPT = high pressure turbine.

^bLPT = low pressure turbine.

TABLE II Summary of Fault Detection Results from Healthy Data

	SST		SPRT
	Total Number of Fault Predictions	Percentage of Faults During Simulation	Total Number of Fault Predictions	Percentage of Faults During Simulation
SS	0	0%	0	0%
LF	356	2.99%	370	3.11%

Fig. 9. Fault detection and fuzzy classification results using leaking SS data.

TABLE III Summary of Fault Detection Results from Leaking Data

	SST		SPRT
	Total Number of Fault Predictions	Percentage of Faults During Simulation	Total Number of Fault Predictions	Percentage of Faults During Simulation
SS	3981	33.46%	4460	37.49%
LF	4557	38.30%	4958	41.67%

TABLE IV Summary of Fault Detection Results from Actuator Data

	SST		SPRT
	Total Number of Fault Predictions	Percentage of Faults During Simulation	Total Number of Fault Predictions	Percentage of Faults During Simulation
SS	3277	27.54%	3877	32.59%
LF	3199	26.89%	3835	32.23%

Fig. 10. Fault detection and fuzzy classification results using actuator LF data.